If you're starting out with Data Science this is a good question to ask yourself. After all you want to be immediately employable and also be efficient with your own time.
Data science is an interdisciplinary field where scientific techniques from statistics, mathematics, and computer science are used to analyze data and solve problems more accurately and effectively.
It is no wonder, then, that languages such as R and Python, with their extensive packages and libraries that support statistical methods and machine learning algorithms are cornerstones of the data science revolution. Often times, beginners find it hard to decide which language to learn first. This post will help you make that decision.
Letâ€™s take a look on both languages :-
R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S. The project was conceived in 1992, with an initial version released in 1995 and a stable beta version in 2000.
R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme.S was created by John Chambers while at Bell Labs. There are some important differences, but much of the code written for S runs unaltered.
It was created with the intention of making data analysis, statistical models and graphical models easier. R has a large repository of packages called CRAN that users routinely contribute to.
R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror.
One of Râ€™s main strengths is that it has a very active community that provides ample support to users via mailing lists, StackOverFlow forums, and very extensive documentation of all its packages. R has a slightly quirky syntax which can be hard to pick up for beginners but is especially suited for people from a statistical and research background looking to get started with creating their models quickly.
Python is a widely used high-level programming language for general-purpose programming, created by Guido van Rossum and first released in 1991.
It is usually the preferred language for people who looking to get into data analysis. It is a very flexible language, making it great for production level work and, like R, has libraries of packages around statistics and machine learning in PyPi, the repository of Python packages. It has great community support, although being a general purpose language it is not all concentrated around data science.
Python features a dynamic type system and automatic memory management and supports multiple programming paradigms, including object-oriented, imperative, functional programming, and procedural styles. It has a large and comprehensive standard library.
The biggest advantage to using Python is the availability of packages such as Theano, Keras, scikit-learn that are important machine learning and deep learning libraries used by both academic research purposes as well as for commercial intent.
Site Links:- Python Online Training
As a problem solver, data science practitioners need to have a versatile set of tools as part of their repertory. While learning both R and Python is ideal, given that R makes data cleaning and manipulation a very easy task while Python is better for building models on larger data sets and scale, we all have to begin somewhere. And the right choice for you can be determined by the following factors :â€“
If you have any programming experience prior to learning data science, our recommendation would be for you to learn Python. Its clear syntax would be easy for you to take up; and with it being a general purpose language, youâ€™d have the added flexibility for building novel stuff. Even a complete novice is advised to learn Python, as it is one of the most beginner friendly languages in Computer Science, being the most popular introductory teaching language in the top U.S. universities (Communications of the ACM article, 2014). R code gets to the point more quickly and is less verbose as well, but it has a quirky syntax that would be difficult to learn for both hardcore programmers and beginners alike. We recommend this course for those interested in learning Python programming.
Having a background in statistics or mathematics makes R a better choice for you. This is because R is a domain specific language created specifically for statistics, making its usage intuitive for people with a degree in statistics. R was created by statisticians and made with other statisticians in mind, so having a grasp of statistical analysis makes the transition into this language all the more easy.
As a data analyst/business analyst/financial analyst, your focus would be on extracting the most information out of your data, without needing to create a product out of your content. For this reason, learning R and a database language like SQL would serve you better as R is great for working with tabular data on a single system/server and has great libraries like ggplot2 for easy visualizations.
But a data scientist has different requirements, as theyâ€™re expected to carry out analysis as well as create products such as machine learning engines that work on the database of a website or a software. This would require both software development as well as predictive modelling work which can be better accomplished by a general purpose language like Python. These principles would apply across all industries.
Deep Learning is the trending topic du jour and anyone with an interest in contributing to the growth of artificial intelligence technology should be learning Python. Its overwhelming popularity for both machine learning, as well as deep learning, comes from the fact that Python acts as an interface between the programmer and lower level languages like C/C++, this making it very easy for experimenting, creating models and debugging without compromising on computational speed (as the machine uses C/C++and CUDA technology to build the models). This makes Python a very accessible language for mathematicians and statisticians looking to create neural network models without having to start creating them from scratch due to the pre-existing frameworks provided by Python.
As you can see, the deeper you wish to get into data science and machine learning, the more it makes sense for your to opt for Python, though R has its own advantages as well. Ultimately, having a thorough understanding of both, each languageâ€™s limitations and strengths is the best approach to learning these two unique languages. With that said, we suggest data science enthusiasts make a choice thatâ€™s suitable for their needs and aspirations.